Cubic Regressions
Good Fit Curve with Regression
￦3,300
1.0for iPhone, iPad
Age Rating
Cubic Regressions 스크린 샷
About Cubic Regressions
There are two Cubic regression calculators:
1. The standard Cubic regression calculator.
2. The Cubic regression calculator with predicted value.
They cover the Cubic regression equation, the Quadratic and Linear regression equations(for reference only).
Cubic regression equation function has the form:
y = a + bx + cx² + dx³
where a, b, c, d are called the coefficient of the cubic regression model.
If d = 0, y = a + bx + cx², it will have the dataset with U shape pattern.
If c = 0 and d = 0, y = a + bx , it will have the dataset with linear shape pattern.
If the dataset does not fall into the perfect linear or the parabolic pattern, it will be more appropriate to use the Cubic regression equation.
It is useful in various fields, such as:
finance, physics, engineering, and social sciences, where there have nonlinear relationships between variables.
Note: we need to stick the value of predicted value of x lies within the minimum and maximum values of array of x data.
Together with the linear and quadratic regression equations, it is a great tool to visualize the whole picture of the dataset analysis.
Highlighted features are:
1. A short learning curve.
2. Showing all equations with options for comparison.
3. Data will be rearranged in ascending order internally.
4. An unique data entry method with error detection.
5. A well formatted chart with options for different selections.
6. Results can be emailed.
7. It can handle 40 sets of data.
1. The standard Cubic regression calculator.
2. The Cubic regression calculator with predicted value.
They cover the Cubic regression equation, the Quadratic and Linear regression equations(for reference only).
Cubic regression equation function has the form:
y = a + bx + cx² + dx³
where a, b, c, d are called the coefficient of the cubic regression model.
If d = 0, y = a + bx + cx², it will have the dataset with U shape pattern.
If c = 0 and d = 0, y = a + bx , it will have the dataset with linear shape pattern.
If the dataset does not fall into the perfect linear or the parabolic pattern, it will be more appropriate to use the Cubic regression equation.
It is useful in various fields, such as:
finance, physics, engineering, and social sciences, where there have nonlinear relationships between variables.
Note: we need to stick the value of predicted value of x lies within the minimum and maximum values of array of x data.
Together with the linear and quadratic regression equations, it is a great tool to visualize the whole picture of the dataset analysis.
Highlighted features are:
1. A short learning curve.
2. Showing all equations with options for comparison.
3. Data will be rearranged in ascending order internally.
4. An unique data entry method with error detection.
5. A well formatted chart with options for different selections.
6. Results can be emailed.
7. It can handle 40 sets of data.
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최신 버전 1.0의 새로운 기능
Last updated on Jul 3, 2023
Version History
1.0
Jul 3, 2023
Cubic Regressions FAQ
제한된 국가 또는 지역에서 Cubic Regressions를 다운로드하는 방법을 알아보려면 여기를 클릭하십시오.
Cubic Regressions의 최소 요구 사항을 보려면 다음 목록을 확인하십시오.
iPhone
iOS 16.0 이상 필요.
iPad
iPadOS 16.0 이상 필요.
Cubic Regressions은 다음 언어를 지원합니다. 영어
